A variety of different techniques exist for categorizing or classifying documents. One kind of classification technique uses a supervised classifier, such as Support Vector Machine (SVM) or Naïve Bayes. Generally speaking, supervised classifiers input feature vectors for a number of labeled training samples, i.e., labeled as to whether or not they belong to a category. Then, based on such training information, the classifier generates a function for mapping an arbitrary feature vector into a decision as to whether or not the corresponding document belongs in the category. When a new unlabeled document or other object (or, more specifically, its feature vector) is input, the function is applied to determine whether the object belongs in the category.
However, such conventional techniques often have drawbacks. For example, most conventional techniques often are inadequate when there are a large number of potential categories and processing time is an important factor, either because some action must be taken in real time or because a large number of documents must be processed.